Time series objectification system and method

• US 20060161071A1
• Filed: 11/16/2005
• Published: 07/20/2006
• Est. Priority Date: 01/27/1997
• Status: Abandoned Application

• Current Assignee: Lawrence A. Lynn
• Original Assignee: Lawrence A. Lynn
• Inventors: Lynn, Eric N., Lynn, Lawrence A.
• Application Number:
  US11/280,559
• Publication Number:
  US 20060161071A1
• Time in Patent Office: Days
• US Class Current: 600/538
• CPC Class Codes: A61B 5/00 Measuring for diagnostic purposes radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00; Identification of persons
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61B 5/145 Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/412 Detecting or monitoring sepsis
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence

Abstract

A system and method for data processing is disclosed. One example comprises accessing data representative of a time series of at least one component of a parameter, and defining a plurality of programmatic objects that each represent a segment of the time series. Another example comprises a memory that is adapted to store data representative of a time series of at least one component of a parameter, and a processor that is adapted to create a plurality of programmatic objects that each represent a segment of the time series.

Claims

1. A method for processing data, comprising:

accessing data representative of a time series of at least one component of a parameter; and

defining a plurality of programmatic objects that each represent a segment of the time series.

2. The method recited in claim 1, comprising evaluating the plurality of programmatic objects to identify a pattern that corresponds to a known condition.

3. The method recited in claim 1, comprising defining a first group of the plurality of programmatic objects to have a first level of a hierarchy.

4. The method recited in claim 3, comprising defining a second group of the plurality of programmatic objects to have a second level of the hierarchy, the second level of the hierarchy being subordinate to the first level of the hierarchy.

5. The method recited in claim 3, wherein the hierarchy comprises an inheritance hierarchy.

6. The method recited in claim 3, wherein the hierarchy comprises a relational hierarchy.

7. The method recited in claim 1, wherein each of the plurality of programmatic objects is of a uniform object type.

8. The method recited in claim 1, wherein the plurality of programmatic objects comprises different object types.

9. The method recited in claim 1, wherein each of the plurality of programmatic objects is sequential.

10. The method recited in claim 1, wherein each of the plurality of programmatic objects is overlapping.

11. The method recited in claim 1, comprising defining a first one of the plurality of programmatic objects to encapsulate at least a second one of the plurality of programmatic objects.

12. The method recited in claim 1, wherein at least one at least one of the plurality of programmatic objects describes a unipolar event.

13. The method recited in claim 1, wherein at least one of the plurality of programmatic objects describes a bipolar event.

14. The method recited in claim 1, wherein at least one of the plurality of programmatic objects describes a corresponding reciprocation event.

15. The method recited in claim 14, comprising defining a group of the plurality of reciprocation events to comprise an inheritance hierarchy.

16. The method recited in claim 14, comprising defining a group of the plurality of reciprocation events to comprise a relational hierarchy.

17. The method recited in claim 14, wherein the plurality of reciprocation events comprises reciprocation events of different reciprocation types.

18. The method recited in claim 14, wherein at least one of the plurality of reciprocation events encapsulate at least another of the plurality of reciprocation events.

19. The method recited in claim 18, wherein the at least another of the plurality of reciprocation events has a duration that is longer than the at least one of the plurality of reciprocation events.

20. The method recited in claim 18, wherein the at least another of the plurality of reciprocation events has a duration that is shorter than the at least one of the plurality of reciprocation events.

21. The method recited in claim 1, wherein at least one of the plurality of programmatic objects corresponds to a drug infusion time segment.

22. The method recited in claim 1, wherein the plurality of programmatic objects corresponds to a physiologic occurrence.

23. A method for processing data, comprising:

accessing data representative of a plurality of time series, each of the plurality of time series corresponding to at least one component of a parameter; and

defining a plurality of programmatic objects that each represent a segment of at least one of the plurality of time series.

24. The method recited in claim 23, wherein at least one of the plurality of programmatic objects describes a corresponding reciprocation event.

25. The method recited in claim 24, comprising defining a first one of the plurality of reciprocation events to encapsulate at least a second one of the plurality of reciprocation events.

26. The method recited in claim 25, wherein the plurality of time series comprises a first time series and a second time series, the at least a first one of the plurality of reciprocation events being derived from the first time series and the at least a second one of the plurality of reciprocation events being derived from the second time series.

27. The method recited in claim 23, wherein the plurality of time series comprises a first time series and a second time series and at least one of the plurality of programmatic objects associated with the first time series describes a corresponding reciprocation event.

28. The method recited in claim 27, comprising defining the corresponding reciprocation event to encapsulate a first one of the programmatic objects associated with the second time series.

29. A method for processing data, comprising:

accessing data representative of a plurality of time series, each of the plurality of time series corresponding to at least one component of a parameter; and

defining a plurality of programmatic objects that each represent a segment of at least one of the plurality of time series along the plurality of time series.

30. A method for processing data, comprising:

accessing data representative of a time series of at least one component of a parameter;

defining a plurality of programmatic objects that each represent a segment of the time series, at least one of the plurality of programmatic objects being a reciprocation object derived from a tidal variation; and

defining as least another of the plurality of programmatic objects as a variation object that encapsulates the reciprocation object.

31. The method recited in claim 30, wherein the tidal variation comprises a pulsation.

32. The method recited in claim 31, wherein the pulsation comprises a plethysmographic pulsation.

33. The method recited in claim 30, wherein the tidal variation comprises a deviation in oxygen saturation.

34. The method recited in claim 30, wherein the reciprocation object comprises coupled inhalation and exhalation flow events.

35. The method recited in claim 30, wherein the tidal variation comprises a gas flow deviation.

36. A data processing system, comprising:

a memory that is adapted to store data representative of a time series of at least one component of a parameter; and

a processor that is adapted to create a plurality of programmatic objects that each represent a segment of the time series.

37. The data processing system recited in claim 36, wherein the processor is adapted to evaluate the plurality of programmatic objects to identify a pattern that corresponds to a known condition.

38. The data processing system recited in claim 37, wherein the processor is adapted to provide an indication responsive to identifying the pattern.

39. A data processing system, comprising:

means for accessing data representative of a time series of at least one component of a parameter; and

means for defining a plurality of programmatic objects that each represent a segment of the time series.

40. A tangible machine-readable medium comprising:

code adapted to access stored data representative of a time series of at least one component of a parameter; and

code adapted to create a plurality of programmatic objects that each represent a segment of the time series.

41. The tangible machine-readable medium recited in claim 40, comprising:

code adapted to evaluate the plurality of programmatic objects to identify a pattern that corresponds to a known condition.

42. A method of analyzing a signal corresponding to a physiologic parameter of a patient, the method comprising:

dividing the signal into a series of programmatic objects, each corresponding to a portion of the signal; and

building a plurality of first event objects, each indicating a change of the signal in a first direction.

43. The method recited in claim 42, comprising building a plurality of second event objects, each indicating a change in the magnitude of the signal in a second direction.

44. The method recited in claim 43, comprising evaluating the first event objects and the second event objects to identify corresponding events representative of a series of reciprocations.

45. The method recited in claim 44, comprising determining if the series of reciprocations corresponds to a known condition.

46. The method recited in claim 42, wherein the plurality of first event objects and the plurality of second event objects comprise multiple dipole objects.

47. The method recited in claim 42, comprising providing an indication if the series of reciprocations corresponds to a known condition.